The MABWA Project

The goal of the MABWA project is to produce an implementation of PyFL that is much faster than the current lazy interpreter, that repeatedly decodes the expression tree. The current interpreter produces Basic PyFL, with all the advanced features (like while loops) implemented by translation. Our strategy is to translate Basic PyFL into the machine language for a FORTH-like stack-based abstract machine.

 

The MABWA machine [+]

The MABWA machine has a conventional FORTH like design, with a reverse polish instruction language. To add 2 and 3 you push 2 then 3 on the implicit value stack, then execute the add instruction that pops the top two elements of the stack, adds them, then pushes the result on the stack.

 

Code Blocks [-]

The 'commands' begin and end delimit code blocks, sequences of instructions that will be manipulated but not immediately executed. The MABWA machine pushes these two blocks onto the stack when it encounters them. They become the top two elements of the stack.

The next three commands test whether x is less than y and leave the boolean result on the stack.

The if command pops the top of the stack (it expects this to be a boolean) and examines it. If this test is true, it pops and discards the top of the stack then pops the element underneath (which should be a code block). It begins executing this block. If the boolean is false it again pops both code blocks but begins executing the second, which was on top of the stack.

 

The MABWA Assembler [+]

The next stage is to assemble the MABWA code into machine language. The result is still text, but at a lower level and more concise. For example, addition is denoted by +, and load becomes three instructions.

 

Evaluation of Variables [+]

The pseudo-instruction " causes the name of the variable (a string) to be pushed on the stack without being examined (as an instruction). Then the instruction $ looks up the definition of the variable - a code block (not shown) which is then executed.

 

Function Calls [+]

Environments come into play during evaluation of function calls. There are two environments, the environment in which the function was called and that in which it was defined. The body of the function is evaluated in the defining environment but the actuals are evaluated in the calling environment.

 

  We plan to write a test interpreter in Python to check out the logic. But for speed, the ultimate goal, we'll use Apple's Swift.